E1 - Análise de dados e Regressão Linear: Preço de imóveis

Considere a base de dados "Ames Housing Dataset". Com o objetivo de desenvolver um modelo de predição do preço do imóvel, desenvolva os itens a seguir e entregue a análise em arquivo do tipo powerpoint ou pdf

  • Importando as bibliotecas
import pandas as pd
import numpy as np
import statsmodels
import seaborn
from matplotlib import pyplot as plt
pd.options.display.max_columns = 100
  • Importando a base de dados
df = pd.read_csv('base_1ah.csv')
print(df.shape)
(1460, 81)
  • Visualizando uma amostra da base_1ah
 df.head()
Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
0 1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub Inside Gtl CollgCr Norm Norm 1Fam 2Story 7 5 2003 2003 Gable CompShg VinylSd VinylSd BrkFace 196.0 Gd TA PConc Gd TA No GLQ 706 Unf 0 150 856 GasA Ex Y SBrkr 856 854 0 1710 1 0 2 1 3 1 Gd 8 Typ 0 NaN Attchd 2003.0 RFn 2 548 TA TA Y 0 61 0 0 0 0 NaN NaN NaN 0 2 2008 WD Normal 208500
1 2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub FR2 Gtl Veenker Feedr Norm 1Fam 1Story 6 8 1976 1976 Gable CompShg MetalSd MetalSd None 0.0 TA TA CBlock Gd TA Gd ALQ 978 Unf 0 284 1262 GasA Ex Y SBrkr 1262 0 0 1262 0 1 2 0 3 1 TA 6 Typ 1 TA Attchd 1976.0 RFn 2 460 TA TA Y 298 0 0 0 0 0 NaN NaN NaN 0 5 2007 WD Normal 181500
2 3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub Inside Gtl CollgCr Norm Norm 1Fam 2Story 7 5 2001 2002 Gable CompShg VinylSd VinylSd BrkFace 162.0 Gd TA PConc Gd TA Mn GLQ 486 Unf 0 434 920 GasA Ex Y SBrkr 920 866 0 1786 1 0 2 1 3 1 Gd 6 Typ 1 TA Attchd 2001.0 RFn 2 608 TA TA Y 0 42 0 0 0 0 NaN NaN NaN 0 9 2008 WD Normal 223500
3 4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub Corner Gtl Crawfor Norm Norm 1Fam 2Story 7 5 1915 1970 Gable CompShg Wd Sdng Wd Shng None 0.0 TA TA BrkTil TA Gd No ALQ 216 Unf 0 540 756 GasA Gd Y SBrkr 961 756 0 1717 1 0 1 0 3 1 Gd 7 Typ 1 Gd Detchd 1998.0 Unf 3 642 TA TA Y 0 35 272 0 0 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
4 5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub FR2 Gtl NoRidge Norm Norm 1Fam 2Story 8 5 2000 2000 Gable CompShg VinylSd VinylSd BrkFace 350.0 Gd TA PConc Gd TA Av GLQ 655 Unf 0 490 1145 GasA Ex Y SBrkr 1145 1053 0 2198 1 0 2 1 4 1 Gd 9 Typ 1 TA Attchd 2000.0 RFn 3 836 TA TA Y 192 84 0 0 0 0 NaN NaN NaN 0 12 2008 WD Normal 250000
 df = df.set_index('Id')

1 - Análise descritiva de variáveis

1.1 Estatísticas descritivas: frequência, proporção, média (𝑥¯), desvio padrão (𝑠), quartis (𝑄1, 𝑥̃ , 𝑄3) (1,0)

Avaliando o tipo das variáveis na base_1ah

 df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1460 entries, 1 to 1460
Data columns (total 80 columns):
 #   Column         Non-Null Count  Dtype
---  ------         --------------  -----
 0   MSSubClass     1460 non-null   int64
 1   MSZoning       1460 non-null   object
 2   LotFrontage    1201 non-null   float64
 3   LotArea        1460 non-null   int64
 4   Street         1460 non-null   object
 5   Alley          91 non-null     object
 6   LotShape       1460 non-null   object
 7   LandContour    1460 non-null   object
 8   Utilities      1460 non-null   object
 9   LotConfig      1460 non-null   object
 10  LandSlope      1460 non-null   object
 11  Neighborhood   1460 non-null   object
 12  Condition1     1460 non-null   object
 13  Condition2     1460 non-null   object
 14  BldgType       1460 non-null   object
 15  HouseStyle     1460 non-null   object
 16  OverallQual    1460 non-null   int64
 17  OverallCond    1460 non-null   int64
 18  YearBuilt      1460 non-null   int64
 19  YearRemodAdd   1460 non-null   int64
 20  RoofStyle      1460 non-null   object
 21  RoofMatl       1460 non-null   object
 22  Exterior1st    1460 non-null   object
 23  Exterior2nd    1460 non-null   object
 24  MasVnrType     1452 non-null   object
 25  MasVnrArea     1452 non-null   float64
 26  ExterQual      1460 non-null   object
 27  ExterCond      1460 non-null   object
 28  Foundation     1460 non-null   object
 29  BsmtQual       1423 non-null   object
 30  BsmtCond       1423 non-null   object
 31  BsmtExposure   1422 non-null   object
 32  BsmtFinType1   1423 non-null   object
 33  BsmtFinSF1     1460 non-null   int64
 34  BsmtFinType2   1422 non-null   object
 35  BsmtFinSF2     1460 non-null   int64
 36  BsmtUnfSF      1460 non-null   int64
 37  TotalBsmtSF    1460 non-null   int64
 38  Heating        1460 non-null   object
 39  HeatingQC      1460 non-null   object
 40  CentralAir     1460 non-null   object
 41  Electrical     1459 non-null   object
 42  1stFlrSF       1460 non-null   int64
 43  2ndFlrSF       1460 non-null   int64
 44  LowQualFinSF   1460 non-null   int64
 45  GrLivArea      1460 non-null   int64
 46  BsmtFullBath   1460 non-null   int64
 47  BsmtHalfBath   1460 non-null   int64
 48  FullBath       1460 non-null   int64
 49  HalfBath       1460 non-null   int64
 50  BedroomAbvGr   1460 non-null   int64
 51  KitchenAbvGr   1460 non-null   int64
 52  KitchenQual    1460 non-null   object
 53  TotRmsAbvGrd   1460 non-null   int64
 54  Functional     1460 non-null   object
 55  Fireplaces     1460 non-null   int64
 56  FireplaceQu    770 non-null    object
 57  GarageType     1379 non-null   object
 58  GarageYrBlt    1379 non-null   float64
 59  GarageFinish   1379 non-null   object
 60  GarageCars     1460 non-null   int64
 61  GarageArea     1460 non-null   int64
 62  GarageQual     1379 non-null   object
 63  GarageCond     1379 non-null   object
 64  PavedDrive     1460 non-null   object
 65  WoodDeckSF     1460 non-null   int64
 66  OpenPorchSF    1460 non-null   int64
 67  EnclosedPorch  1460 non-null   int64
 68  3SsnPorch      1460 non-null   int64
 69  ScreenPorch    1460 non-null   int64
 70  PoolArea       1460 non-null   int64
 71  PoolQC         7 non-null      object
 72  Fence          281 non-null    object
 73  MiscFeature    54 non-null     object
 74  MiscVal        1460 non-null   int64
 75  MoSold         1460 non-null   int64
 76  YrSold         1460 non-null   int64
 77  SaleType       1460 non-null   object
 78  SaleCondition  1460 non-null   object
 79  SalePrice      1460 non-null   int64
dtypes: float64(3), int64(34), object(43)
memory usage: 923.9+ KB

Análise descritiva para variáveis numéricas

 df.describe()
MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr TotRmsAbvGrd Fireplaces GarageYrBlt GarageCars GarageArea WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold SalePrice
count 1460.000000 1201.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1452.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1379.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 56.897260 70.049958 10516.828082 6.099315 5.575342 1971.267808 1984.865753 103.685262 443.639726 46.549315 567.240411 1057.429452 1162.626712 346.992466 5.844521 1515.463699 0.425342 0.057534 1.565068 0.382877 2.866438 1.046575 6.517808 0.613014 1978.506164 1.767123 472.980137 94.244521 46.660274 21.954110 3.409589 15.060959 2.758904 43.489041 6.321918 2007.815753 180921.195890
std 42.300571 24.284752 9981.264932 1.382997 1.112799 30.202904 20.645407 181.066207 456.098091 161.319273 441.866955 438.705324 386.587738 436.528436 48.623081 525.480383 0.518911 0.238753 0.550916 0.502885 0.815778 0.220338 1.625393 0.644666 24.689725 0.747315 213.804841 125.338794 66.256028 61.119149 29.317331 55.757415 40.177307 496.123024 2.703626 1.328095 79442.502883
min 20.000000 21.000000 1300.000000 1.000000 1.000000 1872.000000 1950.000000 0.000000 0.000000 0.000000 0.000000 0.000000 334.000000 0.000000 0.000000 334.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 1900.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 2006.000000 34900.000000
25% 20.000000 59.000000 7553.500000 5.000000 5.000000 1954.000000 1967.000000 0.000000 0.000000 0.000000 223.000000 795.750000 882.000000 0.000000 0.000000 1129.500000 0.000000 0.000000 1.000000 0.000000 2.000000 1.000000 5.000000 0.000000 1961.000000 1.000000 334.500000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 5.000000 2007.000000 129975.000000
50% 50.000000 69.000000 9478.500000 6.000000 5.000000 1973.000000 1994.000000 0.000000 383.500000 0.000000 477.500000 991.500000 1087.000000 0.000000 0.000000 1464.000000 0.000000 0.000000 2.000000 0.000000 3.000000 1.000000 6.000000 1.000000 1980.000000 2.000000 480.000000 0.000000 25.000000 0.000000 0.000000 0.000000 0.000000 0.000000 6.000000 2008.000000 163000.000000
75% 70.000000 80.000000 11601.500000 7.000000 6.000000 2000.000000 2004.000000 166.000000 712.250000 0.000000 808.000000 1298.250000 1391.250000 728.000000 0.000000 1776.750000 1.000000 0.000000 2.000000 1.000000 3.000000 1.000000 7.000000 1.000000 2002.000000 2.000000 576.000000 168.000000 68.000000 0.000000 0.000000 0.000000 0.000000 0.000000 8.000000 2009.000000 214000.000000
max 190.000000 313.000000 215245.000000 10.000000 9.000000 2010.000000 2010.000000 1600.000000 5644.000000 1474.000000 2336.000000 6110.000000 4692.000000 2065.000000 572.000000 5642.000000 3.000000 2.000000 3.000000 2.000000 8.000000 3.000000 14.000000 3.000000 2010.000000 4.000000 1418.000000 857.000000 547.000000 552.000000 508.000000 480.000000 738.000000 15500.000000 12.000000 2010.000000 755000.000000

Análise descritiva das variáveis categóricas

df.describe(include=object)
MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2 Heating HeatingQC CentralAir Electrical KitchenQual Functional FireplaceQu GarageType GarageFinish GarageQual GarageCond PavedDrive PoolQC Fence MiscFeature SaleType SaleCondition
count 1460 1460 91 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1452 1460 1460 1460 1423 1423 1422 1423 1422 1460 1460 1460 1459 1460 1460 770 1379 1379 1379 1379 1460 7 281 54 1460 1460
unique 5 2 2 4 4 2 5 3 25 9 8 5 8 6 8 15 16 4 4 5 6 4 4 4 6 6 6 5 2 5 4 7 5 6 3 5 5 3 3 4 4 9 6
top RL Pave Grvl Reg Lvl AllPub Inside Gtl NAmes Norm Norm 1Fam 1Story Gable CompShg VinylSd VinylSd None TA TA PConc TA TA No Unf Unf GasA Ex Y SBrkr TA Typ Gd Attchd Unf TA TA Y Gd MnPrv Shed WD Normal
freq 1151 1454 50 925 1311 1459 1052 1382 225 1260 1445 1220 726 1141 1434 515 504 864 906 1282 647 649 1311 953 430 1256 1428 741 1365 1334 735 1360 380 870 605 1311 1326 1340 3 157 49 1267 1198
for i, x in enumerate(df.dtypes):
  if x == 'object':
    print(pd.crosstab(index=df[df.columns[i]], columns='freq', dropna=False))
    print('')
col_0     freq
MSZoning
C (all)     10
FV          65
RH          16
RL        1151
RM         218

col_0   freq
Street
Grvl       6
Pave    1454

col_0  freq
Alley
Grvl     50
Pave     41

col_0     freq
LotShape
IR1        484
IR2         41
IR3         10
Reg        925

col_0        freq
LandContour
Bnk            63
HLS            50
Low            36
Lvl          1311

col_0      freq
Utilities
AllPub     1459
NoSeWa        1

col_0      freq
LotConfig
Corner      263
CulDSac      94
FR2          47
FR3           4
Inside     1052

col_0      freq
LandSlope
Gtl        1382
Mod          65
Sev          13

col_0         freq
Neighborhood
Blmngtn         17
Blueste          2
BrDale          16
BrkSide         58
ClearCr         28
CollgCr        150
Crawfor         51
Edwards        100
Gilbert         79
IDOTRR          37
MeadowV         17
Mitchel         49
NAmes          225
NPkVill          9
NWAmes          73
NoRidge         41
NridgHt         77
OldTown        113
SWISU           25
Sawyer          74
SawyerW         59
Somerst         86
StoneBr         25
Timber          38
Veenker         11

col_0       freq
Condition1
Artery        48
Feedr         81
Norm        1260
PosA           8
PosN          19
RRAe          11
RRAn          26
RRNe           2
RRNn           5

col_0       freq
Condition2
Artery         2
Feedr          6
Norm        1445
PosA           1
PosN           2
RRAe           1
RRAn           1
RRNn           2

col_0     freq
BldgType
1Fam      1220
2fmCon      31
Duplex      52
Twnhs       43
TwnhsE     114

col_0       freq
HouseStyle
1.5Fin       154
1.5Unf        14
1Story       726
2.5Fin         8
2.5Unf        11
2Story       445
SFoyer        37
SLvl          65

col_0      freq
RoofStyle
Flat         13
Gable      1141
Gambrel      11
Hip         286
Mansard       7
Shed          2

col_0     freq
RoofMatl
ClyTile      1
CompShg   1434
Membran      1
Metal        1
Roll         1
Tar&Grv     11
WdShake      5
WdShngl      6

col_0        freq
Exterior1st
AsbShng        20
AsphShn         1
BrkComm         2
BrkFace        50
CBlock          1
CemntBd        61
HdBoard       222
ImStucc         1
MetalSd       220
Plywood       108
Stone           2
Stucco         25
VinylSd       515
Wd Sdng       206
WdShing        26

col_0        freq
Exterior2nd
AsbShng        20
AsphShn         3
Brk Cmn         7
BrkFace        25
CBlock          1
CmentBd        60
HdBoard       207
ImStucc        10
MetalSd       214
Other           1
Plywood       142
Stone           5
Stucco         26
VinylSd       504
Wd Sdng       197
Wd Shng        38

col_0       freq
MasVnrType
BrkCmn        15
BrkFace      445
None         864
Stone        128

col_0      freq
ExterQual
Ex           52
Fa           14
Gd          488
TA          906

col_0      freq
ExterCond
Ex            3
Fa           28
Gd          146
Po            1
TA         1282

col_0       freq
Foundation
BrkTil       146
CBlock       634
PConc        647
Slab          24
Stone          6
Wood           3

col_0     freq
BsmtQual
Ex         121
Fa          35
Gd         618
TA         649

col_0     freq
BsmtCond
Fa          45
Gd          65
Po           2
TA        1311

col_0         freq
BsmtExposure
Av             221
Gd             134
Mn             114
No             953

col_0         freq
BsmtFinType1
ALQ            220
BLQ            148
GLQ            418
LwQ             74
Rec            133
Unf            430

col_0         freq
BsmtFinType2
ALQ             19
BLQ             33
GLQ             14
LwQ             46
Rec             54
Unf           1256

col_0    freq
Heating
Floor       1
GasA     1428
GasW       18
Grav        7
OthW        2
Wall        4

col_0      freq
HeatingQC
Ex          741
Fa           49
Gd          241
Po            1
TA          428

col_0       freq
CentralAir
N             95
Y           1365

col_0       freq
Electrical
FuseA         94
FuseF         27
FuseP          3
Mix            1
SBrkr       1334

col_0        freq
KitchenQual
Ex            100
Fa             39
Gd            586
TA            735

col_0       freq
Functional
Maj1          14
Maj2           5
Min1          31
Min2          34
Mod           15
Sev            1
Typ         1360

col_0        freq
FireplaceQu
Ex             24
Fa             33
Gd            380
Po             20
TA            313

col_0       freq
GarageType
2Types         6
Attchd       870
Basment       19
BuiltIn       88
CarPort        9
Detchd       387

col_0         freq
GarageFinish
Fin            352
RFn            422
Unf            605

col_0       freq
GarageQual
Ex             3
Fa            48
Gd            14
Po             3
TA          1311

col_0       freq
GarageCond
Ex             2
Fa            35
Gd             9
Po             7
TA          1326

col_0       freq
PavedDrive
N             90
P             30
Y           1340

col_0   freq
PoolQC
Ex         2
Fa         2
Gd         3

col_0  freq
Fence
GdPrv    59
GdWo     54
MnPrv   157
MnWw     11

col_0        freq
MiscFeature
Gar2            2
Othr            2
Shed           49
TenC            1

col_0     freq
SaleType
COD         43
CWD          4
Con          2
ConLD        9
ConLI        5
ConLw        5
New        122
Oth          3
WD        1267

col_0          freq
SaleCondition
Abnorml         101
AdjLand           4
Alloca           12
Family           20
Normal         1198
Partial         125

1.2 Gráficos como: Gráficos de colunas, BoxPlot, dispersão (2,0)

Para gráficos numéricos plotamos boxplot e para categóricos gráficos de barra.

for i, x in enumerate(df.dtypes):
   if x == 'int64' or x == 'float64':
       plt.figure(i)
       seaborn.boxplot(y = df[df.columns[i]])
   elif x == 'int64' or x == 'object':
       plt.figure(i)
       seaborn.barplot(x = df[df.columns[i]], y = range(0,len(df)) )
/Users/karinseeder/anaconda3/lib/python3.7/site-
packages/matplotlib/pyplot.py:514: RuntimeWarning: More than 20
figures have been opened. Figures created through the pyplot interface
(`matplotlib.pyplot.figure`) are retained until explicitly closed and
may consume too much memory. (To control this warning, see the rcParam
`figure.max_open_warning`).
  max_open_warning, RuntimeWarning)
from pandas.plotting import scatter_matrix
scatter_matrix(df, figsize=(16,16))
array([[<matplotlib.axes._subplots.AxesSubplot object at
0x7fb1b97c8390>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1aacbdf28>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1aac73f28>,
        ...,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ab2b1470>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ab2d99e8>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ab302f60>],
       [<matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ab332518>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ab358a90>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ab35f048>,
        ...,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1aba09208>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1aba31780>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1aba59cf8>],
       [<matplotlib.axes._subplots.AxesSubplot object at
0x7fb1aba882b0>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1abab0828>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1abad8da0>,
        ...,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1d5b62278>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1d5ae3e48>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1a93b4390>],
       ...,
       [<matplotlib.axes._subplots.AxesSubplot object at
0x7fb1b9583278>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1b95ac7f0>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1b9703d68>,
        ...,
        <matplotlib.axes._subplots.AxesSubplot object at
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        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ba5714e0>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ba598a58>],
       [<matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ba5bffd0>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ba5f1588>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1ba61ab00>,
        ...,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1baca6cc0>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1bacd6278>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1bacff7f0>],
       [<matplotlib.axes._subplots.AxesSubplot object at
0x7fb1bae26d68>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1bae59320>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1bae81898>,
        ...,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1bb563a58>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1bb58dfd0>,
        <matplotlib.axes._subplots.AxesSubplot object at
0x7fb1bb5bd588>]],
      dtype=object)

2 - Análise de correlações (𝑟𝑥𝑖,𝑥𝑗) 2.1 Correlograma (1,5) 2.2 Análise sobre correlações significativas (1,5) 3 - Desenvolvimento de modelo de Regressão utilizando Regressão Linear com o método de mínimos quadrados ordinários. Apresente as características do desenvolvimento: amostras, medidas de avaliação do modelo... 3.1 Determinação do tamanho da amostra de teste (0,5) 3.2 Medidas de desempenho para amostra de treino e para amostra de teste: 𝑅2, 𝑅𝑀𝑆𝐸, 𝑀𝐴𝐸 (2,0) 3.3 Descrição do modelo final, discussão sobre a significância obtida para os coeficientes, análise de resíduos (1,5)